Method of generating field regions for agricultural data analysis based on conditional data file generation

ABSTRACT

Systems and methods are provided for generating a plurality of data files, by: displaying a field map corresponding to an agricultural field through a graphical user interface, selecting multiple locations on the field map, identifying, for each selected location, a corresponding data file, generating and displaying a geographic region comprising locations in the identified corresponding data files, and updating the graphical user interface to include a data panel corresponding to the geographic region.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 119 of U.S.Provisional Application No. 62/936,750, filed Nov. 18, 2019, the entirecontents of which are hereby incorporated by reference as if fully setforth within.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015-2020 The Climate Corporation.

FIELD OF THE DISCLOSURE

One technical field of the present disclosure is computer-generatedgraphical user interfaces as applied to agricultural data analysis.Another technical field is transformation and use of agricultural dataacquired during traversal of fields by agricultural apparatus such asplanters or tractors.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Growers commonly perform field trials in which different seeds orhybrids are planted nearby in the same field, often in alternating oradjacent sets of rows, to evaluate how different hybrids performcompared to others under the same weather conditions and geographiclocations. Field trials may involve different planting practices,different fertilization practices, or other forms of differentialtreatment.

Field trials often result in creating and storing large amounts of dataabout what practices were performed in which locations at what times,and the results of those practices in terms of yield or other metrics.During or after such field trials, growers may wish to compare the datafor different regions of a field, for example to evaluate theperformance of one treatment or hybrid versus another. However, presentpractice does not provide all possible convenient methods for definingthe bounds of a region for data analysis.

For example, existing drawing tools require defining bounds freehandusing a finger on a touchscreen yet the personnel performing thisoperation may lack the manual dexterity needed to accurately draw apolygon or other graphical boundary for a field, especially underin-field conditions in the cab of a machine. It may be difficult toselect only relevant details when not in the cab, and difficult torecall the details later of where passes occurred. Improved techniquesto define field regions for data analysis are needed.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7 illustrates an example process that may be programmed toimplement defining field regions based on pass data.

FIG. 8 , FIG. 9 , FIG. 10 , FIG. 11 illustrate example screen displaysthat may be generated and displayed by a cab computer, for example underprogram control using the process of FIG. 7 .

FIG. 12 illustrates an example process that may be programmed toimplement defining field regions using local groupings.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

-   -   1. GENERAL OVERVIEW    -   2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM        -   2.1. STRUCTURAL OVERVIEW        -   2.2. APPLICATION PROGRAM OVERVIEW        -   2.3. DATA INGEST TO THE COMPUTER SYSTEM        -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING        -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW    -   3. EXAMPLE PROCESS OF DEFINING FIELD REGIONS BASED ON PASS DATA

1. General Overview

Growers use planters or sprayers, either self-powered or towed behind atractor or other apparatus, to apply different hybrids or applydifferent treatments in rows in the field. As this equipment traverses afield, it typically repeatedly crosses the field in opposing directionsin movements termed passes. One pass across a field may comprise one ormore planted rows or one or more treatments that are the same. A returnpass can be the same or different. Apparatus can start and stop a passwithin the field at any point. The equipment may be fitted with globalpositioning system (GPS) receivers that continuously generatelatitude-longitude data as the apparatus starts, conducts or completespasses. This data may be locally stored in a cab computer, thentransmitted via wired or wireless links to removable storage devices,other computers, networked resources or cloud computing centers.

In an embodiment, growers who perform field trials with planters orsprayers may use pass data, which is generated when the equipmenttraverses a field, to define a field region. The bounds and annotateddetails of a field region may be edited, but in an embodiment, thebounds and attributes are created principally by recording areas of thefield that are covered while operating a planter or sprayer. Being ableto record areas while in the cab provides an improved, convenientmethod, supported by computer execution, to ensure that a desired fieldregion and corresponding data is accurately defined.

In some embodiments, while in the cab operating a planter or sprayer, agrower can select an option, which is graphically displayed by a cabcomputer, to create or record a field region, enter details of a fieldtrial, make several passes in the field, and then tap stop or done. Thiscauses creating a new field region based on the places in the fieldwhere the equipment traveled during this duration of time whilerecording. These field trials may be named, saved and ready to report onor view again at another time in the growing season.

Alternatively, after one or more passes are complete, pass data may bedisplayed in the graphical user interface of the cab computer, and thegrower can select prior passes graphically, adjust endpoints, andcollect more than one pass in a region.

Further details of embodiments, aspects and features of these approacheswill become apparent from the disclosure as a whole.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) chemical application data (for example, pesticide, herbicide,fungicide, other substance or mixture of substances intended for use asa plant regulator, defoliant, or desiccant, application date, amount,source, method), (g) irrigation data (for example, application date,amount, source, method), (h) weather data (for example, precipitation,rainfall rate, predicted rainfall, water runoff rate region,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, aerial vehiclesincluding unmanned aerial vehicles, and any other item of physicalmachinery or hardware, typically mobile machinery, and which may be usedin tasks associated with agriculture. In some embodiments, a single unitof apparatus 111 may comprise a plurality of sensors 112 that arecoupled locally in a network on the apparatus; controller area network(CAN) is example of such a network that can be installed in combines,harvesters, sprayers, and cultivators. Application controller 114 iscommunicatively coupled to agricultural intelligence computer system 130via the network(s) 109 and is programmed or configured to receive one ormore scripts that are used to control an operating parameter of anagricultural vehicle or implement from the agricultural intelligencecomputer system 130. For instance, a controller area network (CAN) businterface may be used to enable communications from the agriculturalintelligence computer system 130 to the agricultural apparatus 111, suchas how the CLIMATE FIELDVIEW DRIVE, available from The ClimateCorporation, San Francisco, California, is used. Sensor data may consistof the same type of information as field data 106. In some embodiments,remote sensors 112 may not be fixed to an agricultural apparatus 111 butmay be remotely located in the field and may communicate with network109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1 . The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, distributed databases, and any otherstructured collection of records or data that is stored in a computersystem. Examples of RDBMS's include, but are not limited to including,ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQLdatabases. However, any database may be used that enables the systemsand methods described herein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5 , a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5 , the top two timelines have the “Springapplied” program selected, which includes an application of 150 lbs N/acin early April. The data manager may provide an interface for editing aprogram. In an embodiment, when a particular program is edited, eachfield that has selected the particular program is edited. For example,in FIG. 5 , if the “Spring applied” program is edited to reduce theapplication of nitrogen to 130 lbs N/ac, the top two fields may beupdated with a reduced application of nitrogen based on the editedprogram.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5 , the interface may update to indicatethat the “Spring applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Spring applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6 , a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6 . To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored or calculated output valuesthat can serve as the basis of computer-implemented recommendations,output data displays, or machine control, among other things. Persons ofskill in the field find it convenient to express models usingmathematical equations, but that form of expression does not confine themodels disclosed herein to abstract concepts; instead, each model hereinhas a practical application in a computer in the form of storedexecutable instructions and data that implement the model using thecomputer. The model may include a model of past events on the one ormore fields, a model of the current status of the one or more fields,and/or a model of predicted events on the one or more fields. Model andfield data may be stored in data structures in memory, rows in adatabase table, in flat files or spreadsheets, or other forms of storeddigital data.

In an embodiment, planting management logic 135 and field regiondefinition logic 136 comprises a set of one or more pages of mainmemory, such as RAM, in the agricultural intelligence computer system130 into which executable instructions have been loaded and which whenexecuted cause the agricultural intelligence computer system to performthe functions or operations that are described herein with reference tothose modules. In an embodiment, planting management logic 135 isprogrammed to receive data specifying planting or treatment practicesfrom apparatus 111 and/or cab computer 115, perform yield calculationsor computation of other metrics and store the resulting data inrepository 160. Thus, planting management logic 135 provides acloud-based computational resource for collecting large quantities ofdata originally generated in the field at apparatus 111 and calculatingcomplex metrics based on the data. The field region definition logic 136is integrated with cab computer 115 and is programmed to implement theprocess of FIG. 7 and to generate the screen displays shown in FIG. 8 ,FIG. 9 , FIG. 10 , FIG. 11 .

In each case, the instructions may be in machine executable code in theinstruction set of a CPU and may have been compiled based upon sourcecode written in JAVA, C, C++, OBJECTIVE-C, or any other human-readableprogramming language or environment, alone or in combination withscripts in JAVASCRIPT, other scripting languages and other programmingsource text. The term “pages” is intended to refer broadly to any regionwithin main memory and the specific terminology used in a system mayvary depending on the memory architecture or processor architecture. Inanother embodiment, each of planting management logic 135 and fieldregion definition logic 136 also may represent one or more files orprojects of source code that are digitally stored in a mass storagedevice such as non-volatile RAM or disk storage, in the agriculturalintelligence computer system 130 or a separate repository system, whichwhen compiled or interpreted cause generating executable instructionswhich when executed cause the agricultural intelligence computer systemto perform the functions or operations that are described herein withreference to those modules. In other words, the drawing figure mayrepresent the manner in which programmers or software developersorganize and arrange source code for later compilation into anexecutable, or interpretation into bytecode or the equivalent, forexecution by the agricultural intelligence computer system 130.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4 . The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML, and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114 which include an irrigation sensor and/or irrigationcontroller. In response to receiving data indicating that applicationcontroller 114 released water onto the one or more fields, field managercomputing device 104 may send field data 106 to agriculturalintelligence computer system 130 indicating that water was released onthe one or more fields. Field data 106 identified in this disclosure maybe input and communicated using electronic digital data that iscommunicated between computing devices using parameterized URLs overHTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,California. The CLIMATE FIELDVIEW application, or other applications,may be modified, extended, or adapted to include features, functions,and programming that have not been disclosed earlier than the filingdate of this disclosure. In one embodiment, the mobile applicationcomprises an integrated software platform that allows a grower to makefact-based decisions for their operation because it combines historicaldata about the grower's fields with any other data that the growerwishes to compare. The combinations and comparisons may be performed inreal time and are based upon scientific models that provide potentialscenarios to permit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2 , each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprises account,fields, data ingestion, sharing instructions 202 which are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shape files,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, e-mail with attachment, external APIs that push data to themobile application, or instructions that call APIs of external systemsto pull data into the mobile application. In one embodiment, mobilecomputer application 200 comprises a data inbox. In response toreceiving a selection of the data inbox, the mobile computer application200 may display a graphical user interface for manually uploading datafiles and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of fertilizer application zones and/or imagesgenerated from subfield soil data, such as data obtained from sensors,at a high spatial resolution (as fine as millimeters or smallerdepending on sensor proximity and resolution); upload of existinggrower-defined zones; providing a graph of plant nutrient availabilityand/or a map to enable tuning application(s) of nitrogen across multiplezones; output of scripts to drive machinery; tools for mass data entryand adjustment; and/or maps for data visualization, among others. “Massdata entry,” in this context, may mean entering data once and thenapplying the same data to multiple fields and/or zones that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields and/or zones of the same grower,but such mass data entry applies to the entry of any type of field datainto the mobile computer application 200. For example, nitrogeninstructions 210 may be programmed to accept definitions of nitrogenapplication and practices programs and to accept user input specifyingto apply those programs across multiple fields. “Nitrogen applicationprograms,” in this context, refers to stored, named sets of data thatassociates: a name, color code or other identifier, one or more dates ofapplication, types of material or product for each of the dates andamounts, method of application or incorporation such as injected orbroadcast, and/or amounts or rates of application for each of the dates,crop or hybrid that is the subject of the application, among others.“Nitrogen practices programs,” in this context, refer to stored, namedsets of data that associates: a practices name; a previous crop; atillage system; a date of primarily tillage; one or more previoustillage systems that were used; one or more indicators of applicationtype, such as manure, that were used. Nitrogen instructions 210 also maybe programmed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium), application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, yield differential, hybrid, population, SSURGOzone, soil test properties, or elevation, among others. Programmedreports and analysis may include yield variability analysis, treatmenteffect estimation, benchmarking of yield and other metrics against othergrowers based on anonymized data collected from many growers, or datafor seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2 , in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the field manager computing device104, agricultural apparatus 111, or sensors 112 in the field and ingest,manage, and provide transfer of location-based scouting observations tothe system 130 based on the location of the agricultural apparatus 111or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from one or more fields. In an embodiment, applicationcontroller 114 is programmed or configured to receive instructions fromagricultural intelligence computer system 130. Application controller114 may also be programmed or configured to control an operatingparameter of an agricultural vehicle or implement. For example, anapplication controller may be programmed or configured to control anoperating parameter of a vehicle, such as a tractor, planting equipment,tillage equipment, fertilizer or insecticide equipment, harvesterequipment, or other farm implements such as a water valve. Otherembodiments may use any combination of sensors and controllers, of whichthe following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, California, may be operatedto export data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus; otherelectromagnetic radiation emitters and reflected electromagneticradiation detection apparatus. Such controllers may include guidance ormotor control apparatus, control surface controllers, cameracontrollers, or controllers programmed to turn on, operate, obtain datafrom, manage and configure any of the foregoing sensors. Examples aredisclosed in U.S. patent application Ser. No. 14/831,165 and the presentdisclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in U.S. Provisional Application No. 62/154,207,filed on Apr. 29, 2015, U.S. Provisional Application No. 62/175,160,filed on Jun. 12, 2015, U.S. Provisional Application No. 62/198,060,filed on Jul. 28, 2015, and U.S. Provisional Application No. 62/220,852,filed on Sep. 18, 2015, may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4. Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, fertilizer recommendations,fungicide recommendations, pesticide recommendations, harvestingrecommendations and other crop management recommendations. The agronomicfactors may also be used to estimate one or more crop related results,such as agronomic yield. The agronomic yield of a crop is an estimate ofquantity of the crop that is produced, or in some examples the revenueor profit obtained from the produced crop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise, distorting effects, and confounding factorswithin the agronomic data including measured outliers that couldadversely affect received field data values. Embodiments of agronomicdata preprocessing may include, but are not limited to, removing datavalues commonly associated with outlier data values, specific measureddata points that are known to unnecessarily skew other data values, datasmoothing, aggregation, or sampling techniques used to remove or reduceadditive or multiplicative effects from noise, and other filtering ordata derivation techniques used to provide clear distinctions betweenpositive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared and/or validated usingone or more comparison techniques, such as, but not limited to, rootmean square error with leave-one-out cross validation (RMSECV), meanabsolute error, and mean percentage error. For example, RMSECV can crossvalidate agronomic models by comparing predicted agronomic propertyvalues created by the agronomic model against historical agronomicproperty values collected and analyzed. In an embodiment, the agronomicdataset evaluation logic is used as a feedback loop where agronomicdatasets that do not meet configured quality thresholds are used duringfuture data subset selection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5. Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infrared signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through theworldwide packet data communication network now commonly referred to asthe “Internet” 428. Local network 422 and Internet 428 both useelectrical, electromagnetic or optical signals that carry digital datastreams. The signals through the various networks and the signals onnetwork link 420 and through communication interface 418, which carrythe digital data to and from computer system 400, are example forms oftransmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. Example Process of Defining Field Regions Based on Pass Data

3.1 Defining Field Regions During Operation of Apparatus

A first embodiment comprises a computer-implemented process that isexecuted during field operation of agricultural apparatus 111 (FIG. 1 ).In this embodiment, while in the cab operating a planter, sprayer orother apparatus 111, a grower 102 interacts with cab computer 115, whichexecutes field region definition logic 136. The cab computer 115comprises a touchscreen display that outputs a graphical user interface.The field region definition logic 136 is programmed to implement thefollowing.

1. The grower 102 selects an option that is graphically displayed by thecab computer 115 to create or record a field region. In response, fieldregion definition logic 136 causes displaying a field details panel.

2. Optionally, the grower can enter details of a field trial in thefield details panel. Example details include identification of a hybrid.Other data may be automatically collected by cab computer 115, such asthe present date and time and/or geo-location data from a GPS receiveron the apparatus 111 and coupled to the cab computer.

3. The grower 102 makes one or more passes in the field by operating theapparatus 111. These passes may involve seeding, planting, spraying orother treatment operations. During any such passes, GPS position dataand other data such as seeding rate, treatment type, date and time isautomatically continuously collected by cab computer 115 and receiversor sensors, such as sensors 112, coupled to the cab computer and storedin local memory.

4. The grower 102 returns attention to the cab computer and taps a STOPor DONE icon, graphical button or link in the GUI. In response, fieldregion definition logic 136 creates and stores data for a new fieldregion based on the places in the field where the equipment traveledduring this duration of time while recording. These field trials may benamed, saved and ready to report on or view again at another time in thegrowing season.

3.2 Defining Field Regions after Pass Data is Collected

FIG. 7 illustrates an example process that may be programmed toimplement defining field regions based on pass data. FIG. 8 , FIG. 9 ,FIG. 10 , FIG. 11 illustrate example screen displays that may begenerated and displayed by a cab computer, for example under programcontrol using the process of FIG. 7 . Referring first to FIG. 7 , in anembodiment, at step 702, field region definition logic 136 is programmedto execute as a cab computer application, including displaying a startscreen.

At step 704, field region definition logic 136 is programmed to receiveinput to select an agricultural field from among a plurality of definedfields of a grower. For example, the grower may apply touchscreen inputto a touchscreen display coupled to the cab computer to select aparticular named, previously defined field using a pull-down menu, listor other GUI widget. In response, field region definition logic 136 isprogrammed to retrieve data for the selected field and update thedisplay. Data may be retrieved from local memory of the cab computer115, or from the repository of FIG. 1 via wireless networking.

At step 706, field region definition logic 136 is programmed to receiveinput to select a field map for the selected field. For example, memoryof the cab computer 115 may store multiple different field maps thathave been previously generated for the selected field. In response,field region definition logic 136 is programmed to display the selectedfield map in the display of the cab computer. FIG. 8 , FIG. 9 , FIG. 10, FIG. 11 illustrate graphical displays of example field maps that maybe displayed in some embodiments, as further described herein. In someembodiments, the field map may be a planting map.

At step 708, field region definition logic 136 is programmed to receiveinput to select action options for fields. In response to the selection,field region definition logic 136 is programmed to update the display tographically show a list of the options. Referring now to FIG. 8 , in oneembodiment, the preceding steps may cause generating and displaying afield map 802 in the GUI of the cab computer. In an embodiment, fieldmap 802 comprises zoom controls 803, field details options 804, 806,808, action options 811, and an options list 815. In an embodiment, zoomcontrols 803 are programmed to zoom a display of the field map 802 tohigher or lower levels of resolution in response to touchscreen input tothe controls by tapping on the controls for example. In an embodiment,field map 802 comprises a plurality of visually distinct rows 810, 812representing rows of a field that have been planted with different seedsor hybrids or associated with different row treatments such as differentlevels or kinds of fertilizer. In some embodiments, rows associated witha particular hybrid may be visually displayed in a distinct manner suchas using a particular color for a particular hybrid. Other embodimentsmay use shading, hatching or other visual treatment to indicate hybridsor treatments.

In an embodiment, a field ID control 804 is programmed to permitselecting among different fields for which data has been stored. In anembodiment, a planting control 806 is programmed as a pull-down menu GUIwidget that is associated with different kinds of plantings of the samefield in different seasons. In an embodiment, a hybrid control 808 isprogrammed as a pull-down menu GUI widget that is associated withdifferent hybrids that are defined in stored data and available toindicate as the hybrid that was planted in the field or in a row.Selecting any of the controls 804, 806, 808 causes field regiondefinition logic 136 to update the screen display 802 to include apull-down list of available data items corresponding to the selectedcontrol. Data to populate a pull-down list may be retrieved from storagethat is coupled to cab computer 115. Data items shown in a list forfield ID control 804 comprise any named field that the grower haspreviously identified in terms of location, size and other parameters.Data items shown in a list for planting control 806 comprise any namedplanting that the grower created to correspond to a planting session ofthe current season. Data items shown in hybrid control 808 may compriseall hybrids that the grower has previously purchased from vendors ofhybrids and/or an inventory of hybrids that a particular vendorspecifies.

In an embodiment, action options 811 comprises a set of links, graphicalbuttons or other widgets which when selected cause activating displaycontrol functions, data display functions or option lists. In someembodiments, links in the action options 811 are programmed to modify azoom level, scope or size of the screen display 802; to center thescreen display at a then-current location of a planter, sprayer or otherapparatus that is coupled to the cab computer 115; to display a tableview of field data, rather than a planting view; to return to a home orstart screen; and other controls. In an embodiment, an options control814 in the action options 811 is programmed to display the options list815 when selected. In an embodiment, options list 815 comprises a pop-uplist, as shown, of functions such as displaying a planting summary,transmitting field files to other computers, sharing or printing theplanting map, marking a location in the planting map, or creating afield region as indicated by a Create Field Region button 816.

At step 710, field region definition logic 136 is programmed to receivetouchscreen input to select a field creation option from among optionsin the list. For example, touchscreen input selects the Create FieldRegion button 816 of FIG. 8 . In response, at step 712, field regiondefinition logic 136 is programmed to update the display to graphicallyshow options for one or more methods to create a field region. Referringnow to FIG. 9 , in one embodiment, step 712 comprises updating screendisplay 802 to display a field region selection bar 902 comprising aplurality of graphical buttons including a pass selection button 904.The selection bar 902 may include graphical buttons that are programmedto enable digitally creating a field region definition by freehanddrawing or defining a polygon by selecting points in the field map shownin display 802. The selection bar 902 also may comprise an exit control906 which when selected causes the field region definition logic 136 toupdate the display by closing or removing the selection bar. These areillustrated at the bottom of FIG. 10 to illustrate a clear example butcould be in any position in the screen display 802 in other embodiments.

At step 714, field region definition logic 136 is programmed to receivetouchscreen input indicating creating a field region based on equipmentpasses. For example, in one embodiment touchscreen input indicatesselecting Select Passes button 904 as seen in FIG. 9 . In response, atstep 716, field region definition logic 136 is programmed to retrievestored equipment pass data specifying geo-location and other data forstart and end points of one or more equipment passes, the storedequipment pass data having been created and stored based on signals fromequipment during previous operation in the field.

Furthermore, at step 720, field region definition logic 136 isprogrammed to update display of field map to graphically indicate one ormore equipment passes based on the equipment pass data and superimposedon the field map. Referring now to FIG. 10 , in one embodiment executingstep 720 comprises the field region definition logic 136 updating thescreen display 802 to visually indicate a plurality of equipment passes1002, 1004, 1006 each having a start point and endpoint indicated bycontrols 1008, 1009. An equipment pass, in this context, comprisesgraphical lines, bars, or polygons in the screen display 802 thatindicate the actual path that a planter, sprayer or other apparatuspreviously traversed in the field. In an embodiment, each equipment pass1002, 1004, 1006 is graphically drawn in the screen display 802 basedupon receiving geo-location data and metadata specifying start and stoppoints of passes that were generated and stored in storage of cabcomputer 115 as the equipment in which the cab computer is installedactually traversed a field. Thus, the passes shown in FIG. 10 aredisplayed based on data for actual passes conducted by the sameequipment and cab computer that generates the display FIG. 10 .

The display of FIG. 10 further comprises a prompt bar 1010 comprising aDone control 1012 and a Cancel control. These are illustrated at thebottom of FIG. 10 to illustrate a clear example but could be in anyposition in the screen display 802 in other embodiments.

Controls 1008, 1009, in one embodiment, are graphical controlsresponsive to touchscreen input that can be dragged or moved in thedisplay 802 under touch control. By default, the controls 1008, 1009 aredisplayed in positions of display 802 that correspond to actualgeo-locations at which a planter, sprayer or other apparatus started orstopped a pass. In some cases, a control, such as control 1009, may bedisplayed within a row of a planting map; this position indicates thatapparatus stopped and started at that point. In some cases, two controlsmay be displayed spaced apart in a planting row; this can occur, forexample, when spraying stops, followed by an equipment movement toanother location in the row, followed by resuming spraying. Otherinterruption of equipment operation, for planting, treatment or otheroperations, may cause similar displays of controls based on the datathat is collected for those operations and their geo-locations.

At step 722, field region definition logic 136 is programmed to receivetouchscreen input in the field map to select one or more of the passes,change one or more bounds of the selected one or more passes, and signalcompletion of defining a region. For example, in an embodiment,touchscreen input taps on the pass 1006, drags the endpoint controls1008 of the pass to adjust their position, and selects Done button 1012to signal that defining a region is complete. Touchscreen input alsocould specify a plurality of the passes 1002, 1004, 1006 for a largerfield region consisting of multiple passes. Touchscreen input also couldadjust different controls 1008 of any zero or more of the passes 1002,1004, 1006 to specify boundaries of the multiple-pass field region. Inthis manner, a region of a field can be graphically defined quickly byreceiving touchscreen input that interacts with passes that have beendisplayed based on previously collected pass data corresponding toapparatus movement. Rather than performing a freehand drawing on screendisplay 802 to define a region or drawing a polygon via repeated taps orcontrols to define edges and vertices of the polygon, the grower 102 canrely on existing data that already defines passes in terms of all orportions of rows in the field or other boundaries. Consequently, thepresent techniques permit the cab computer 115 to obtain a definition ofa field region using fewer items of touchscreen input and fewer new datapoints, thus reducing the amount of computer processing needed toprocess user input and reducing the amount of storage needed to define aregion.

At step 724, field region definition logic 136 is programmed to retrievefield performance data constrained to the specified field region andupdate the display to include a data panel for the field performancedata. Referring now to FIG. 11 , in one embodiment, step 724 maycomprise generating and displaying a field region data panel 1102comprising a basic data bar 1104 and a data table 1106. While FIG. 11depicts the data panel 1102 as a display including a data bar and datatable, other embodiments may include different types of displays. Forexample, data panel 1102 may be an external report which may bedisplayed on a screen, stored in memory, or transmitted to an externalcomputing device. Additionally or alternatively, the data panel 1102 maybe a modal-less display which may be displayed on a screen with otherdisplays.

In an embodiment, the basic data bar 1104 displays fundamental datavalues such as size, moisture content and actual or predicted yield foronly the field region that has been defined in the preceding steps. Thedata shown in field region data panel 1102 may be locally calculated atcab computer 115 by retrieving stored data for the field as a whole,selecting values from the data based upon the field region boundariesthat have been defined using equipment passes, and calculating valuesfor display based on the selected values. In this manner, data in fieldregion data panel is constrained to comprise a dynamically updateddisplay that is tied to the field region that has been defined and doesnot reflect values for the field as a whole. Consequently, grower 102 orother computers can receive a dataset that is constrained to a specifiedfield region even if the grower has not previously calculated or seenthose values. Therefore, the grower 102 or other computers can receive adataset that did not exist before but that has been dynamicallycalculated from a larger dataset of values for the entire field,constrained to values associated with the particular field region thatwas defined.

As an example, FIG. 11 shows data table 1106 comprising data for averageyield and area in acres for specified hybrids and soil types that arewithin the defined region. However, other embodiments may generate anddisplay data for other metrics, such as nutrient content, seedpopulation, average temperature, or other metrics associated with thefield region and already stored in local storage of the cab computer 115as part of the planting map or other field data definitions.

At step 726, field region definition logic 136 is programmed tooptionally receive input specifying saving the field region and inresponse, save data defining the field region based on the selectedequipment passes in association with a name or label. For example, in anembodiment, touchscreen input selects a Save Field Region button 1108 inFIG. 11 . In response, cab computer 115 creates and stores a record inlocal storage that persistently stores the boundaries of the sub-fieldregion that has been defined based on the equipment pass data, inassociation with metadata such as a name, growing season, date-time andso forth. This data may be automatically assigned by retrieving valuesfrom the system clock and/or inheriting values from the planting mapthat had been displayed to select passes for the field region. Or, step726 may comprise generating and displaying an input dialog comprising aneditable input field that prompts grower 102 to enter data values forthe field region.

3.3 Defining Field Regions Using Logical Groupings

FIG. 12 illustrates an example process that may be programmed toimplement defining field regions using local groupings. The methodillustrated in FIG. 12 can be performed at the agricultural intelligencecomputing system 130 and/or at the cab computer 115.

At step 1202, a computing system identifies a start condition. The startcondition may be one that is explicitly input, such as receiving inputthrough the cab computer to begin recording a pass. For example, the cabcomputer may display an option to begin recording a pass and/or portionof a pass. In response to receiving input selecting the option, the cabcomputer may identify the start condition.

Additionally or alternatively, the computing system may identify thestart condition from one or more actions of the agricultural implementand/or vehicle. For example, one or more sensors or applicationcontrollers may be programmed or configured to control an agriculturalimplement and/or vehicle and/or monitor one or more actions of theagricultural implement and/or vehicle. Data from the sensors orapplication controllers may be sent to the cab computer and/oragricultural intelligence computer system. The cab computer and/oragricultural intelligence computer system may identify a start conditionwhen the agricultural vehicle and/or implement begins one or moreoperations while records of the agricultural vehicle and/or implementare not being aggregated into a data file. The one or more operationsmay include moving through the agricultural field, planting on thefield, releasing one or more chemicals onto the field, releasing wateronto the field, harvesting the field, and/or any other operationrelating to crop planting, management, or harvesting.

The computing system may additionally or alternatively identify a startcondition when the agricultural implement and/or vehicle begins a newpass on the agricultural field. The computing system may determine a newpass is being performed if a current heading of the agriculturalimplement and/or vehicle is greater than a threshold angle differentfrom a previous heading. For example, if the heading of an agriculturalvehicle is greater than 45° different than the heading of theagricultural vehicle five seconds prior, the computing system maydetermine a new pass is being initiated. Additionally or alternatively,the computing system may determine a new pass is being initiated basedon a current location of the agricultural vehicle in reference to aprescription map identifying passes in the field.

In an embodiment, the computing system identifies a start conditionbased on a height of a header of a combine. For example, the computingsystem may store a predetermined and/or modeled threshold height value.When the header of the combine lowers below the threshold height value,the computing system may determine that a new pass is being performedand/or may identify the start condition. Conversely, when the headerheight raises above the threshold value, the computing system maydetermine a pass is being completed and/or may identify a stopcondition.

At step 1204, a new data file is generated. For example, the cabcomputer or agricultural intelligence computer system may generate a newfile for recording data from the agricultural implement and/oragricultural vehicle in response to identifying the start condition. Asa practical example, the agricultural intelligence computer system maycreate a “.dat” file when an agricultural vehicle begins moving throughthe field after having been stopped for a particular period of time.

At step 1206, the system begins recording data of an apparatus movingthrough an agricultural field into the data file. For example, thesystem may record a plurality of records into the data file relating tothe agricultural implement and/or vehicle. The records may include oneor more of a temporal component, such as a time a measurement is takenand/or the record is recorded, a separate measurement component, such asa seed population planted, crop harvested, or chemical applied, and alocation component, such as GPS coordinates of the agriculturalimplement and/or vehicle. The system may record a new record at specifictemporal intervals, such as every 0.2 seconds, or at intervals based onthe actions of the implement and/or vehicle, such as number of seedsplanted or distance moved.

At step 1208, the system identifies a stop condition. The stop conditionmay be one that is explicitly input, such as receiving input through thecab computer to stop recording a pass. For example, the cab computer maydisplay an option while recording a pass and/or portion of a pass tostop recording. In response to receiving input selecting the option, thecab computer may identify the stop condition.

Additionally or alternatively, the computing system may identify thestop condition from one or more actions of the agricultural implementand/or vehicle. For example, one or more sensors or applicationcontrollers may be programmed or configured to control an agriculturalimplement and/or vehicle and/or monitor one or more actions of theagricultural implement and/or vehicle. Data from the sensors orapplication controllers may be sent to the cab computer and/oragricultural intelligence computer system. The cab computer and/oragricultural intelligence computer system may identify a stop conditionwhen the agricultural implement and/or vehicle stops an action orchanges an action. For example, the computing system may identify thestop condition when the agricultural implement and/or vehicle stopsmoving for more than a threshold period of time, such as ten seconds,stops planting, harvesting, spraying, or otherwise stopping an operationrelating to crop planting, management, or harvesting for more than athreshold period of time. As another example, the computing system mayidentify the stop condition when the agricultural implement changesdirection by an angle greater than a stored threshold, changes a seedingrate by more than a threshold population, changes a spraying rate bymore than a threshold volume, moves into a different management zone,travels a particular distance, and/or other preprogrammed stopconditions relating to a change in the functioning and/or controlling ofthe agricultural implement and/or vehicle.

At step 1210, the data file is stored. For example, agriculturalintelligence computing system and/or at the cab computer may store adata file comprising recorded data corresponding to the time between thestart condition and the stop condition. In an embodiment, the data fileis created by the cab computer and sent to the agricultural intelligencecomputer system for storage. Additionally or alternatively, the datacaptured from the agricultural implement and/or vehicle may be sent bythe cab computer to the agricultural intelligence computer system whichgenerates data files based on the start and stop conditions.

Steps 1202-1210 may be completed a plurality of times for anagricultural field as the apparatus performs one or more functions onthe agricultural field. For example, a new data file may be created foreach pass, each identified change in planting, movement, or othermanagement activity, and/or each management zone. Thus, multiple datafiles may correspond to a single pass and/or a single data file may spanmultiple passes.

At step 1212, a field map is displayed through a graphical userinterface. For example, a field map may be displayed on the cab computerand/or a client computing device and the cab computer and/or clientcomputing device may receive touchscreen input to select a fieldcreation option from among options in a list of options, as describedwith respect to FIG. 7 . In an embodiment, the list of options includesa “dragging selection option” which is selected through the touchscreeninput.

At step 1214, input is received through the graphical user interfaceselecting multiple locations on the field map. The input may includeindividual taps of multiple points on the map and/or a dragging inputacross a region in the field. A computing system may be programmed orconfigured to identify, for a plurality of tapped locations and/orlocations corresponding to the dragging input, a location on theagricultural field.

At step 1216, a corresponding data file is identified for each selectedlocation. For example, the computing system may identify each data filethat corresponds to a tapped location or location corresponding to thedragging input. As a practical example, if a user initiates a dragginginput across a pass of a field which corresponds to multiple data files,the agricultural intelligence computer system may only select the datafiles that correspond to the dragging input. Thus, input of a point on apass may cause the server computer to select a data file thatcorresponds to less than the full pass and/or select a data file thatcorresponds to locations within multiple passes.

At step 1218, a region bounded by data in the identified correspondingdata files is generated. For example, the computing system may use theselected data files to identify a region which includes the locationsidentified in the selected data files. The regions may be bounded at thelocations at the edges of the data files. Thus, the boundaries of aselection may be different than the boundaries of the field and/or pass.This allows for a logical selection of boundaries from input selectingindividual locations based on the start and stop conditions.

At step 1220, the display is updated to include a data panelcorresponding to the generated region. For example, the computing systemmay retrieve data from the stored data files, such as measurements orother management data, and use the retrieved data to generate a datapanel interface, such as the interface of FIG. 11 described above. Thus,the data panel may correspond to the region defined at step 1218 usingthe data from the data files used to create the region at step 1218.Additionally or alternatively, data from outside the data files used togenerate the region may be used to create the data panel. For instance,the computing system may extract data corresponding to locations in theselected data files from different data files with different boundaries.For example, if the region is created based on data files generatedduring harvesting operations and the data panel includes datacorresponding to planting operations, the computing system may identifyplanting data that corresponds to locations identified in the harvestingdata files.

As a practical example of the method of FIG. 12 , an agriculturalvehicle may begin seeding a field and sending data to an agriculturalintelligence computer system which is based on monitored seeding rates.If the agricultural vehicle is stopped during a particular pass, such asto change the hybrids or adjust a parameter of the vehicle, theagricultural intelligence computer system may identify a stop conditionand store a data file. When the agricultural vehicle begins seedingagain, the agricultural intelligence computer system may generate a newdata file and record data received from the agricultural vehicle in thenew data file. When the agricultural intelligence computer system causesdisplay of the agricultural field on a client computing device andreceives input selecting a location corresponding to the first file, theagricultural intelligence computer system may display a region that isbounded at the location where the agricultural vehicle stopped, as thatlocation would mark the edge of the locations in the stored data file.

By generating and storing data files based on the start and stopconditions, the agricultural intelligence computer system is able togenerate logical boundaries for regions selected through a clientcomputing device. This allows for greater fidelity in displayedinformation and for easier updating of stored data. For example, if thestopping of the agricultural implement was due to a change in thehybrid, a user would be able to easily select a portion of the fieldprior to the change and input the hybrid type used for that portion asthe selected region would be bounded by the location where the vehiclestopped for the change in hybrid.

3.4 Defining Field Regions Chronologically

In an embodiment, the graphical user interface comprises one or moreoptions to select portions of the agricultural field chronologically.The agricultural intelligence computer system and/or cab computer maystore, in one or more data files, a plurality of records, each of whichcomprising data describing at least a physical location of anagricultural implement and/or vehicle and a time at which theagricultural implement and/or vehicle was at or performed an action atthe physical location. For example, a seeding data record may identify alocation of a planter at a particular time, a population planted at thelocation and particular time, and a seed type being planted at thelocation and the particular time.

A cab computer and/or client computing device may cause display of animage of an agricultural field with an option for selecting passesand/or portions of passes chronologically, such as through an option inthe interface of FIG. 9 . A user may select the option for selectingpasses and/or portions of passes chronologically through a display onthe cab computer and/or through a display on a client computing devicewhich indicates the selection to the agricultural intelligence computersystem. In response to receiving the input selecting the option forselecting passes and/or portions of passes chronologically, the cabcomputer and/or agricultural intelligence computer system may causedisplaying a timeline on the interface displayed on the cab computerand/or client computing device with options for selecting a lower boundon the timeline and an upper bound on the timeline, such as through adragging of one or more boundaries of the timeline.

When an upper bound and a lower bound have been selected, the cabcomputer and/or agricultural intelligence computer system may identify,in the stored data records, a plurality of locations that correspond totimes that are within a time period between a time corresponding to theselected lower bound and a time corresponding to the selected upperbound. The agricultural intelligence computer system may generate anoverlay for the map which displays a region which includes the pluralityof locations and/or a data panel corresponding to the plurality oflocations.

In an embodiment, the cab computer and/or agricultural intelligencecomputer system identifies each data file which comprises at least onerecord that includes a time within the time period between the timecorresponding to the selected lower bound and the time corresponding tothe selected upper bound. The cab computer and/or agriculturalintelligence computer system may then generate an overlay for the mapwhich displays a region which includes all locations identified in thestored data files and/or a data panel corresponding to all locationsidentified in the stored data files. The cab computer and/oragricultural intelligence computer system may additionally modify theselection on the timeline by moving the lower bound to the earliest timein the identified data files and/or by moving the upper bound to thelatest time in the identified data files.

In an embodiment, the cab computer and/or agricultural intelligencecomputer system identifies each pass where at least a portion of thepass was performed during a time within the time period between the timecorresponding to the selected lower bound and the time corresponding tothe selected upper bound. The cab computer and/or agriculturalintelligence computer system may then generate an overlay for the mapwhich displays a region which includes all locations within theidentified passes and/or a data panel corresponding to all locations inthe identified passes. The cab computer and/or agricultural intelligencecomputer system may additionally modify the selection on the timeline bymoving the lower bound to the earliest time in the identified passesand/or by moving the upper bound to the latest time in the identifieddata files.

By using a chronological selection, the computing system is able tologically group data records based on when an agricultural implement orvehicle performed a task on an agricultural field. This unique methodfor displaying information improves the use of the graphical userinterface by allowing data to be aggregated based on when anagricultural activity occurred. Thus, if an event occurred at aparticular time, the display could be used to compare results ofagricultural activities prior to the event with results of agriculturalactivities after the event. Additionally, the use of the data files fromsection 3.3 allows the computing system to identify logical boundariesbased on a chronological selection, thereby decreasing the accuracy atwhich a user must select times to produce a request result.

What is claimed is:
 1. A system comprising: one or more processors; and a memory storing instructions which, when executed by the one or more processors, cause performance of: creating a plurality of data files by performing, for each data file of the plurality of data files: identifying a start condition; in response to identifying the start condition, generating a new data file; recording data of an apparatus moving through an agricultural field in the new data file, the data comprising locations of the apparatus during said recording; identifying a stop condition; and in response to identifying the stop condition, storing the new data file, the new data file being a data file in the plurality of data files; displaying a field map corresponding to the agricultural field through a graphical user interface; receiving input through the graphical user interface selecting multiple locations on the field map; for each selected location of the multiple locations, identifying a corresponding data file of the plurality of data files; generating a geographic region comprising locations in the identified corresponding data files, the geographic region being bounded by edges of the locations in the identified corresponding data files; displaying the geographic region through the graphical user interface; and updating the graphical user interface to include a data panel corresponding to the geographic region.
 2. The system of claim 1, wherein the instructions, when executed by the one or more processors, further cause performance of: receiving a plurality of values defining a header of the apparatus at a plurality of times; and determining that the header at a particular time is greater than a threshold angle different from a header at a particular previous time, wherein the start condition is identified in response to determining that the header at the particular time is greater than the threshold angle different from the header at the particular previous time.
 3. The system of claim 1, wherein the instructions, when executed by the one or more processors, further cause performance of: receiving a plurality of values defining a height of a header of the apparatus at a plurality of times; and determining, from the plurality of values, that the height of the header has lowered below a threshold height value, wherein the start condition is identified in response to determining, from the plurality of values, that the height of the header has lowered below the threshold height value.
 4. The system of claim 1, wherein the instructions, when executed by the one or more processors, further cause performance of: storing a prescription map for the agricultural field identifying a plurality of passes in the agricultural field; and determining that a location of the apparatus is within a different pass from a prior location of the apparatus, wherein the start condition is identified in response to determining that the location of the apparatus is within the different pass from the prior location of the apparatus.
 5. The system of claim 1, wherein the instructions, when executed by the one or more processors, further cause performance of: determining that the apparatus has stopped for greater than a threshold period of time, wherein the stop condition is identified in response to determining that the apparatus has stopped for greater than the threshold period of time.
 6. The system of claim 1, wherein the data panel comprises aggregated data relating from each location identified in the one or more of the identified corresponding data files.
 7. The system of claim 6, wherein the aggregated data comprises one or more of planting data, harvesting data, chemical application data, or crop characteristic data.
 8. The system of claim 1, wherein recording data of the apparatus moving through the agricultural field in the new data file comprises recording, for each of a plurality of intervals, a temporal component, a separate measurement component, and a location component.
 9. The system of claim 8, wherein receiving input through the graphical user interface selecting multiple locations on the field map comprises: displaying, through the graphical user interface, a timeline and one or more options for selecting a lower bound on the timeline and an upper bound on the timeline; and receiving input selecting a particular lower bound and a particular upper bound on the timeline; wherein the multiple locations correspond to times between the particular lower bound and the particular upper bound.
 10. The system of claim 9, wherein the instructions, when executed by the one or more processors, further cause performance of: determining that a particular data file of the one or more of the identified corresponding data files comprises an entry with a time component that is prior to the lower bound and, in response, updating the lower bound on the timeline on the graphical user interface to match an earliest time in the particular data file.
 11. A computer-implemented method comprising: creating a plurality of data files by performing, for each data file of the plurality of data files: identifying, at a computing system, a start condition; in response to identifying the start condition, generating a new data file; recording data of an apparatus moving through an agricultural field in the new data file, the data comprising locations of the apparatus during said recording; identifying a stop condition; and in response to identifying the stop condition, storing the new data file, the new data file being a data file in the plurality of data files; displaying a field map corresponding to the agricultural field through a graphical user interface; receiving input through the graphical user interface selecting multiple locations on the field map; for each selected location of the multiple locations, identifying a corresponding data file of the plurality of data files; generating a geographic region comprising locations in the identified corresponding data files, the geographic region being bounded by edges of the locations in the identified corresponding data files; displaying the geographic region through the graphical user interface; and updating the graphical user interface to include a data panel corresponding to the geographic region.
 12. The computer-implemented method of claim 11, further comprising: receiving, at the computing system, a plurality of values defining a header of the apparatus at a plurality of times; and determining that the header at a particular time is greater than a threshold angle different from a header at a particular previous time, wherein the start condition is identified in response to determining that the header at the particular time is greater than the threshold angle different from the header at the particular previous time.
 13. The computer-implemented method of claim 11, further comprising: receiving, at the computing system, a plurality of values defining a height of a header of the apparatus at a plurality of times; and determining, from the plurality of values, that the height of the header has lowered below a threshold height value, wherein the start condition is identified in response to determining, from the plurality of values, that the height of the header has lowered below the threshold height value.
 14. The computer-implemented method of claim 11, further comprising: storing a prescription map for the agricultural field identifying a plurality of passes in the agricultural field; and determining that a location of the apparatus is within a different pass from a prior location of the apparatus, wherein the start condition is identified in response to determining that the location of the apparatus is within the different pass from the prior location of the apparatus.
 15. The computer-implemented method of claim 11, further comprising: determining that the apparatus has stopped for greater than a threshold period of time, wherein the stop condition is identified in response to determining that the apparatus has stopped for greater than the threshold period of time.
 16. The computer-implemented method of claim 11, wherein the data panel comprises aggregated data relating from each location identified in the one or more of the identified corresponding data files.
 17. The computer-implemented method of claim 16, wherein the aggregated data comprises one or more of planting data, harvesting data, chemical application data, or crop characteristic data.
 18. The computer-implemented method of claim 11, wherein recording data of the apparatus moving through the agricultural field in the new data file comprises recording, for each of a plurality of intervals, a temporal component, a separate measurement component, and a location component.
 19. The computer-implemented method of claim 18, wherein receiving input through the graphical user interface selecting multiple locations on the field map comprises: displaying, through the graphical user interface, a timeline and one or more options for selecting a lower bound on the timeline and an upper bound on the timeline; and receiving input selecting a particular lower bound and a particular upper bound on the timeline; wherein the multiple locations correspond to times between the particular lower bound and the particular upper bound.
 20. The method of claim 19, further comprising: determining that a particular data file of the one or more of the identified corresponding data files comprises an entry with a time component that is prior to the lower bound and, in response, updating the lower bound on the timeline on the graphical user interface to match an earliest time in the particular data file. 